Using Synchronic Definitions and Semantic Relations to Classify Semantic Change Types

TL;DR

This paper introduces a model that uses lexical relations and definitions from WordNet to classify semantic change types, improving understanding of how word meanings evolve and aiding semantic change detection.

Contribution

It presents a novel approach combining synset definitions and hierarchy information to classify semantic change types and enhance semantic relatedness and change detection models.

Findings

Effective classification of semantic change types using WordNet data.

Improved models for semantic relatedness and change detection.

Validation on a digitized dataset of semantic change types.

Abstract

There is abundant evidence of the fact that the way words change their meaning can be classified in different types of change, highlighting the relationship between the old and new meanings (among which generalization, specialization and co-hyponymy transfer). In this paper, we present a way of detecting these types of change by constructing a model that leverages information both from synchronic lexical relations and definitions of word meanings. Specifically, we use synset definitions and hierarchy information from WordNet and test it on a digitized version of Blank's (1997) dataset of semantic change types. Finally, we show how the sense relationships can improve models for both approximation of human judgments of semantic relatedness as well as binary Lexical Semantic Change Detection.